Spaces:
Sleeping
Sleeping
| # Copyright (c) OpenMMLab. All rights reserved. | |
| import pytest | |
| import torch | |
| import torch.nn as nn | |
| from mmcv.cnn import NonLocal1d, NonLocal2d, NonLocal3d | |
| from mmcv.cnn.bricks.non_local import _NonLocalNd | |
| def test_nonlocal(): | |
| with pytest.raises(ValueError): | |
| # mode should be in ['embedded_gaussian', 'dot_product'] | |
| _NonLocalNd(3, mode='unsupport_mode') | |
| # _NonLocalNd with zero initialization | |
| _NonLocalNd(3) | |
| _NonLocalNd(3, norm_cfg=dict(type='BN')) | |
| # _NonLocalNd without zero initialization | |
| _NonLocalNd(3, zeros_init=False) | |
| _NonLocalNd(3, norm_cfg=dict(type='BN'), zeros_init=False) | |
| def test_nonlocal3d(): | |
| # NonLocal3d with 'embedded_gaussian' mode | |
| imgs = torch.randn(2, 3, 10, 20, 20) | |
| nonlocal_3d = NonLocal3d(3) | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| # NonLocal is only implemented on gpu in parrots | |
| imgs = imgs.cuda() | |
| nonlocal_3d.cuda() | |
| out = nonlocal_3d(imgs) | |
| assert out.shape == imgs.shape | |
| # NonLocal3d with 'dot_product' mode | |
| nonlocal_3d = NonLocal3d(3, mode='dot_product') | |
| assert nonlocal_3d.mode == 'dot_product' | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| nonlocal_3d.cuda() | |
| out = nonlocal_3d(imgs) | |
| assert out.shape == imgs.shape | |
| # NonLocal3d with 'concatenation' mode | |
| nonlocal_3d = NonLocal3d(3, mode='concatenation') | |
| assert nonlocal_3d.mode == 'concatenation' | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| nonlocal_3d.cuda() | |
| out = nonlocal_3d(imgs) | |
| assert out.shape == imgs.shape | |
| # NonLocal3d with 'gaussian' mode | |
| nonlocal_3d = NonLocal3d(3, mode='gaussian') | |
| assert not hasattr(nonlocal_3d, 'phi') | |
| assert nonlocal_3d.mode == 'gaussian' | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| nonlocal_3d.cuda() | |
| out = nonlocal_3d(imgs) | |
| assert out.shape == imgs.shape | |
| # NonLocal3d with 'gaussian' mode and sub_sample | |
| nonlocal_3d = NonLocal3d(3, mode='gaussian', sub_sample=True) | |
| assert isinstance(nonlocal_3d.g, nn.Sequential) and len(nonlocal_3d.g) == 2 | |
| assert isinstance(nonlocal_3d.g[1], nn.MaxPool3d) | |
| assert nonlocal_3d.g[1].kernel_size == (1, 2, 2) | |
| assert isinstance(nonlocal_3d.phi, nn.MaxPool3d) | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| nonlocal_3d.cuda() | |
| out = nonlocal_3d(imgs) | |
| assert out.shape == imgs.shape | |
| # NonLocal3d with 'dot_product' mode and sub_sample | |
| nonlocal_3d = NonLocal3d(3, mode='dot_product', sub_sample=True) | |
| for m in [nonlocal_3d.g, nonlocal_3d.phi]: | |
| assert isinstance(m, nn.Sequential) and len(m) == 2 | |
| assert isinstance(m[1], nn.MaxPool3d) | |
| assert m[1].kernel_size == (1, 2, 2) | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| nonlocal_3d.cuda() | |
| out = nonlocal_3d(imgs) | |
| assert out.shape == imgs.shape | |
| def test_nonlocal2d(): | |
| # NonLocal2d with 'embedded_gaussian' mode | |
| imgs = torch.randn(2, 3, 20, 20) | |
| nonlocal_2d = NonLocal2d(3) | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| imgs = imgs.cuda() | |
| nonlocal_2d.cuda() | |
| out = nonlocal_2d(imgs) | |
| assert out.shape == imgs.shape | |
| # NonLocal2d with 'dot_product' mode | |
| imgs = torch.randn(2, 3, 20, 20) | |
| nonlocal_2d = NonLocal2d(3, mode='dot_product') | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| imgs = imgs.cuda() | |
| nonlocal_2d.cuda() | |
| out = nonlocal_2d(imgs) | |
| assert out.shape == imgs.shape | |
| # NonLocal2d with 'concatenation' mode | |
| imgs = torch.randn(2, 3, 20, 20) | |
| nonlocal_2d = NonLocal2d(3, mode='concatenation') | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| imgs = imgs.cuda() | |
| nonlocal_2d.cuda() | |
| out = nonlocal_2d(imgs) | |
| assert out.shape == imgs.shape | |
| # NonLocal2d with 'gaussian' mode | |
| imgs = torch.randn(2, 3, 20, 20) | |
| nonlocal_2d = NonLocal2d(3, mode='gaussian') | |
| assert not hasattr(nonlocal_2d, 'phi') | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| imgs = imgs.cuda() | |
| nonlocal_2d.cuda() | |
| out = nonlocal_2d(imgs) | |
| assert out.shape == imgs.shape | |
| # NonLocal2d with 'gaussian' mode and sub_sample | |
| nonlocal_2d = NonLocal2d(3, mode='gaussian', sub_sample=True) | |
| assert isinstance(nonlocal_2d.g, nn.Sequential) and len(nonlocal_2d.g) == 2 | |
| assert isinstance(nonlocal_2d.g[1], nn.MaxPool2d) | |
| assert nonlocal_2d.g[1].kernel_size == (2, 2) | |
| assert isinstance(nonlocal_2d.phi, nn.MaxPool2d) | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| nonlocal_2d.cuda() | |
| out = nonlocal_2d(imgs) | |
| assert out.shape == imgs.shape | |
| # NonLocal2d with 'dot_product' mode and sub_sample | |
| nonlocal_2d = NonLocal2d(3, mode='dot_product', sub_sample=True) | |
| for m in [nonlocal_2d.g, nonlocal_2d.phi]: | |
| assert isinstance(m, nn.Sequential) and len(m) == 2 | |
| assert isinstance(m[1], nn.MaxPool2d) | |
| assert m[1].kernel_size == (2, 2) | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| nonlocal_2d.cuda() | |
| out = nonlocal_2d(imgs) | |
| assert out.shape == imgs.shape | |
| def test_nonlocal1d(): | |
| # NonLocal1d with 'embedded_gaussian' mode | |
| imgs = torch.randn(2, 3, 20) | |
| nonlocal_1d = NonLocal1d(3) | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| imgs = imgs.cuda() | |
| nonlocal_1d.cuda() | |
| out = nonlocal_1d(imgs) | |
| assert out.shape == imgs.shape | |
| # NonLocal1d with 'dot_product' mode | |
| imgs = torch.randn(2, 3, 20) | |
| nonlocal_1d = NonLocal1d(3, mode='dot_product') | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| imgs = imgs.cuda() | |
| nonlocal_1d.cuda() | |
| out = nonlocal_1d(imgs) | |
| assert out.shape == imgs.shape | |
| # NonLocal1d with 'concatenation' mode | |
| imgs = torch.randn(2, 3, 20) | |
| nonlocal_1d = NonLocal1d(3, mode='concatenation') | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| imgs = imgs.cuda() | |
| nonlocal_1d.cuda() | |
| out = nonlocal_1d(imgs) | |
| assert out.shape == imgs.shape | |
| # NonLocal1d with 'gaussian' mode | |
| imgs = torch.randn(2, 3, 20) | |
| nonlocal_1d = NonLocal1d(3, mode='gaussian') | |
| assert not hasattr(nonlocal_1d, 'phi') | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| imgs = imgs.cuda() | |
| nonlocal_1d.cuda() | |
| out = nonlocal_1d(imgs) | |
| assert out.shape == imgs.shape | |
| # NonLocal1d with 'gaussian' mode and sub_sample | |
| nonlocal_1d = NonLocal1d(3, mode='gaussian', sub_sample=True) | |
| assert isinstance(nonlocal_1d.g, nn.Sequential) and len(nonlocal_1d.g) == 2 | |
| assert isinstance(nonlocal_1d.g[1], nn.MaxPool1d) | |
| assert nonlocal_1d.g[1].kernel_size == 2 | |
| assert isinstance(nonlocal_1d.phi, nn.MaxPool1d) | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| nonlocal_1d.cuda() | |
| out = nonlocal_1d(imgs) | |
| assert out.shape == imgs.shape | |
| # NonLocal1d with 'dot_product' mode and sub_sample | |
| nonlocal_1d = NonLocal1d(3, mode='dot_product', sub_sample=True) | |
| for m in [nonlocal_1d.g, nonlocal_1d.phi]: | |
| assert isinstance(m, nn.Sequential) and len(m) == 2 | |
| assert isinstance(m[1], nn.MaxPool1d) | |
| assert m[1].kernel_size == 2 | |
| if torch.__version__ == 'parrots': | |
| if torch.cuda.is_available(): | |
| nonlocal_1d.cuda() | |
| out = nonlocal_1d(imgs) | |
| assert out.shape == imgs.shape | |